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Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review

2025Artigo científico Acesso aberto
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datacite.subject.fosCiências Sociais::Ciências da Educação
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorMaio, Eduardo
dc.contributor.authorAfonso, Pedro
dc.contributor.authorEncarnação, Samuel
dc.contributor.authorMachado, Guilherme
dc.contributor.authorMorgans, Ryland
dc.contributor.authorBarbosa, Tiago M.
dc.contributor.authorMonteiro, António M.
dc.contributor.authorForte, Pedro
dc.contributor.authorFerraz, Ricardo
dc.contributor.authorBranquinho, Luís
dc.date.accessioned2025-10-31T16:46:35Z
dc.date.available2025-10-31T16:46:35Z
dc.date.issued2025
dc.description.abstractFootball, as a dynamic and complex sport, demands an understanding of tactical behaviors to excel in training and competition. Artificial intelligence (AI) has evolutionized the tactical performance analysis in football, offering unprecedented data analytics insights for players, coaches, and analysts. This systematic review aims to examine and map out the current state of research on AI-based tactical behavior, collective dynamics, and movement patterns in football. A total of 2,548 articles were identified following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Population-Intervention-Comparators-Outcomes framework. By synthesizing findings from 32 studies, this review elucidates the available AI-based techniques to analyze tactical behavior and identify the collective dynamic based on artificial neural networks, deep learning, machine learning, and timeseries techniques. Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. Furthermore, collective dynamics and patterns were mapped by graph metrics such as betweenness centrality, eccentricity, efficiency, vulnerability, clustering coefficient, and page rank, expected possession value, pitch control map classifier, computer vision techniques, expected goals, 3D ball trajectories, dangerousity assessment, pass probability model, and total passes attempted. The performance of technicaltactical key indicators was expressed by team possession, team formation, team strategy, team-space control efficiency, determining team formations, coordination patterns, analyzing player interactions, ball trajectories, and pass effectiveness. In conclusion, the AI-based models can effectively reshape the landscape of spatiotemporal tracking data into training and practice routines with real-time decision-making support, performance prediction, match management, tactical-strategic thinking, and training task design. Nevertheless, there are still challenges for the real practical application of AI-based techniques, as well as ethical regulation and the formation of professional profiles that combine sports science, data analytics, computer science, and coaching expertise.por
dc.description.sponsorshipThe authors declare that financial support was received for the research and/or publication of this article. This project was supported by the National Funds through the FCT Portuguese Foundation for Science and Technology (project UID/CED/04748/2020 and UIDB04045/2021), Life Quality Research Center (LQRC-CIEQV), Santar\u00E9m, Portugal; Research Centre in Sports Sciences, Health Sciences and Human Development, Vila Real, Portugal; SPRINT\u2014Sport Physical Activity and Health Research and Innovation Center, Portugal; and Research Center for Active Living and Wellbeing (Livewell), Bragan\u00E7a, Portugal.
dc.identifier.citationTeixeira, José Eduardo; Maio, Eduardo; Afonso, Pedro; Encarnação, Samuel; Machado, Guilherme; Morgans, Ryland; Barbosa, Tiago M.; Monteiro, António M.; Forte, Pedro; Ferraz, Ricardo; Branquinho, Luís (2025). Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review. Frontiers in Sports and Active Living. ISSN 2624-9367. 7, p. 1-23
dc.identifier.doi10.3389/fspor.2025.1569155
dc.identifier.issn2624-9367
dc.identifier.urihttp://hdl.handle.net/10198/34911
dc.language.isoeng
dc.peerreviewedyes
dc.publisherFrontiers Media
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPerformance
dc.subjectTactical analysis
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectDeep learning
dc.subjectAI
dc.titleMapping football tactical behavior and collective dynamics with artificial intelligence: a systematic revieweng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage23
oaire.citation.startPage1
oaire.citation.titleFrontiers in Sports and Active Living
oaire.citation.volume7
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameTeixeira
person.familyNameEncarnação
person.familyNameBarbosa
person.familyNameMonteiro
person.familyNameForte
person.givenNameJosé Eduardo
person.givenNameSamuel
person.givenNameTiago M.
person.givenNameAntónio M.
person.givenNamePedro
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.ciencia-id9416-E2F5-E660
person.identifier.ciencia-id8B11-BDC4-F6FF
person.identifier.ciencia-idC41C-6CCD-A1F0
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0000-0003-2965-2777
person.identifier.orcid0000-0001-7071-2116
person.identifier.orcid0000-0003-4467-1722
person.identifier.orcid0000-0003-0184-6780
person.identifier.scopus-author-id10044856400
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